Approximate Recursive Bayesian Filtering methods for robot visual search
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Visual servoing is an essential enabling technology for robots operating in semi- and un-structured contexts, such as robot assistants working in collaboration with people. However, due to dynamic and unpredictable nature of such environments, existing methods of target tracking can lose visibility of task/target, leading to servo failure. In such situations, it is desirable that the robot reacquire the target in an autonomous/automatic fashion. In this paper we take a fresh look at this problem by examining the simplified case of a pan-tilt mounted camera visually searching for a lost target. We adopt Lost Target Search techniques based on Recursive Bayesian Filtering algorithms that have been applied to other search platforms such as aerial search and rescue. We investigated both an approximate grid-based filter and a sequential Monte Carlo method, namely particle filter. In both cases we use a new sensor-based observation model. The particle filter exhibited superior performance over approximate grid-based filter in our simulations, and was utilized in a follow-on experiment. In the experiment, we improved the particle filter performance by considering the a priori target tracking information in the motion model. Finally, we discuss the implications of this approach to higher degree of freedom robot systems.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it